Unobserved components with stochastic volatility: Simulation‐based estimation and signal extraction
M Li, SJ Koopman - Journal of Applied Econometrics, 2021 - Wiley Online Library
The unobserved components time series model with stochastic volatility has gained much
interest in econometrics, especially for the purpose of modelling and forecasting inflation …
interest in econometrics, especially for the purpose of modelling and forecasting inflation …
A dynamic probabilistic principal components model for the analysis of longitudinal metabolomics data
G Nyamundanda, IC Gormley… - Journal of the Royal …, 2014 - academic.oup.com
In a longitudinal metabolomics study, multiple metabolites are measured from several
observations at many time points. Interest lies in reducing the dimensionality of such data …
observations at many time points. Interest lies in reducing the dimensionality of such data …
“Exact” and approximate methods for Bayesian inference: Stochastic volatility case study
Y Shapovalova - Entropy, 2021 - mdpi.com
We conduct a case study in which we empirically illustrate the performance of different
classes of Bayesian inference methods to estimate stochastic volatility models. In particular …
classes of Bayesian inference methods to estimate stochastic volatility models. In particular …
Variational heteroscedastic volatility model
We propose Variational Heteroscedastic Volatility Model (VHVM)--an end-to-end neural
network architecture capable of modelling heteroscedastic behaviour in multivariate …
network architecture capable of modelling heteroscedastic behaviour in multivariate …
Particle filters for inference of high-dimensional multivariate stochastic volatility models with cross-leverage effects
Y Xu, A Jasra - Foundations of Data Science, 2019 - aimsciences.org
Multivariate stochastic volatility models are a popular and well-known class of models in the
analysis of financial time series because of their abilities to capture the important stylized …
analysis of financial time series because of their abilities to capture the important stylized …
Scalable inference for a full multivariate stochastic volatility model
We introduce a multivariate stochastic volatility model that imposes no restrictions on the
structure of the volatility matrix and treats all its elements as functions of latent stochastic …
structure of the volatility matrix and treats all its elements as functions of latent stochastic …
Measuring and quantifying uncertainty in volatility spillovers: A bayesian approach
Y Shapovalova, M Eichler - Data Science in Science, 2023 - Taylor & Francis
Volatility spillover measures are crucial for studying connectivity of financial time series.
Understanding how financial time series are interconnected can help, for example, portfolio …
Understanding how financial time series are interconnected can help, for example, portfolio …
Updates in Bayesian filtering by continuous projections on a manifold of densities
In this paper, we develop a novel method for approximate continuous-discrete Bayesian
filtering. The projection filtering framework is exploited to develop accurate approximations …
filtering. The projection filtering framework is exploited to develop accurate approximations …
[PDF][PDF] Modelling volatility of Kuala Lumpur composite index (KLCI) using SV and GARCH models
EA Abdullah, SM Zahari, SSR Shariff… - Indonesian Journal of …, 2019 - academia.edu
It is well-known that financial time series exhibits changing variance and this can have
important consequences in formulating economic or financial decisions. In much recent …
important consequences in formulating economic or financial decisions. In much recent …
Scalable inference for a full multivariate stochastic volatility model
P Dellaportas, A Plataniotis, MK Titsias - arXiv preprint arXiv:1510.05257, 2015 - arxiv.org
We introduce a multivariate stochastic volatility model for asset returns that imposes no
restrictions to the structure of the volatility matrix and treats all its elements as functions of …
restrictions to the structure of the volatility matrix and treats all its elements as functions of …